A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data

TL;DR

This paper introduces a hybrid algorithm combining constraint-based and Bayesian methods for learning causal networks from sparse data, effectively searching equivalence classes and scoring models to improve accuracy.

Contribution

The paper proposes a novel hybrid anytime algorithm that integrates constraint-based heuristics with Bayesian scoring for causal model construction from limited data.

Findings

The hybrid algorithm outperforms greedy search variants in accuracy.

It effectively handles sparse data in networks of 15 to 45 nodes.

The approach is validated on randomly generated networks with varying data sizes.

Abstract

We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) using a heuristic based on conventional constraint-based techniques. Each essential graph is then converted into a directed acyclic graph and scored using a Bayesian scoring metric. Two variants of the algorithm are developed and tested using data from randomly generated networks of sizes from 15 to 45 nodes with data sizes ranging from 250 to 2000 records. Both variations are compared to, and found to consistently outperform two variations of greedy search with restarts.